基于CMIP6模式的中国西北地区干旱时空变化

doi:10.13866/j.azr.2024.05.01

干旱区研究 ›› 2024, Vol. 41 ›› Issue (5): 717-729.doi: 10.13866/j.azr.2024.05.01 cstr: 32277.14.j.azr.2024.05.01

基于CMIP6模式的中国西北地区干旱时空变化

山建安¹(), 朱睿¹, 尹振良²^,³(), 杨华庆¹, 张薇¹, 方春爽¹

1.兰州交通大学测绘与地理信息学院，地理国情监测技术应用国家地方联合工程研究中心，甘肃省测绘科学与技术重点实验室，甘肃兰州 730000
2.中国科学院西北生态环境资源研究院，国家冰川冻土沙漠科学数据中心，干旱区生态安全与可持续发展重点实验室，甘肃兰州 730000
3.山东科技大学安全与环境工程学院，山东青岛 266000

收稿日期:2023-11-18 修回日期:2024-01-21 出版日期:2024-05-15 发布日期:2024-05-29
通讯作者: 尹振良. E-mail: yinzhenliang@lzb.ac.cn
作者简介:山建安（1999-），男，硕士研究生，主要从事干旱区气候变化预测. E-mail: 11220853@stu.lzjtu.edu.cn
基金资助:
国家自然科学基金项目(42161018);国家自然科学基金项目(52179026);中国科学院青年创新促进会会员项目(2021424);甘肃省陇原青年创新创业人才（团队）项目(2023);新疆生产建设兵团科技攻关计划项目(2021AB021)

Spatial and temporal variation of drought in Northwest China based on CMIP6 model

SHAN Jian'an¹(), ZHU Rui¹, YIN Zhenliang²^,³(), YANG Huaqing¹, ZHANG Wei¹, FANG Chunshuang¹

1. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Key Laboratory of Science and Technology in Surveying & Mapping, Gansu Province, Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730000, Gansu, China
2. Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China
3. College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266000, Shandong, China

Received:2023-11-18 Revised:2024-01-21 Published:2024-05-15 Online:2024-05-29

摘要/Abstract

摘要：

基于西北地区的152个气象台站和 CMIP6（Coupled Model Intercomparison Project 6）的16个气候模式输出资料，采用RoMBC（Robust Multivariate Bias Correction）方法对CMIP6模式数据进行偏差校正，构建标准化降水蒸散发指数（Standardized Precipitation Evapotranspiration Index，SPEI），分析西北地区历史及未来4种情景下（SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5）的干旱时空分布及变化特征。结果表明：（1）历史和未来情景下西北地区气温和降水均呈显著增加趋势，增温速率为0.15~0.74 ℃·（10a）^-1，降水增加速率为2.71~14.83 mm·（10a）^-1。（2）历史（1975—2014年）西北地区年、季节尺度SPEI整体呈下降趋势，春季下降速率最大，为0.19·（10a）^-1，年、春季、冬季大部分地区干旱趋势加重，干旱频率轻中旱高于重特旱，东部干旱频率高于西部。（3）未来情景下（2020—2100年）SSP1-2.6情景有干旱趋势但无明显干旱特征，其余3种情景干旱次数增加、干旱趋势加重、干旱频率提高，SSP5-8.5情景下干旱最为明显。该研究通过气象数据和模式数据揭示了西北地区的干旱时空发展规律，可为西北地区干旱风险评估、水资源科学管理及农业生产提供依据。

关键词: SPEI, 干旱时空格局, CMIP6, 西北地区

Abstract:

Based on data from 152 meteorological stations in Northwest China and 16 climate models of CMIP6, the CMIP6 model data were bias-corrected using the RoMBC method. The Standardized Precipitation Evapotranspiration Index (SPEI) was then constructed to analyze the spatial and temporal distribution and variation of drought in Northwest China under the historical and future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The results are as follows: (1) Under the historical scenario, the northwest area experienced a notable increase in both the temperature and precipitation. The temperature and precipitation have been rising at a rate of 0.15-0.74 ℃ and 2.71-14.83 mm per decade, respectively, and the same is expected for future scenarios. (2) From 1975 to 2014, the annual and seasonal SPEI in Northwest China decreased overall. The maximum decline rate in spring was 0.19 per decade. Droughts in most areas were increasingly intense throughout the year, particularly in spring and winter. In terms of drought frequency in Northwest China, mild and moderate droughts appeared more than severe and extreme droughts, and this type of natural disaster was more frequent in the east of the country than in the west. (3) From 2020 to 2100, Northwest China is likely to suffer from droughts, but there are no distinct drought characteristics identified in the research under the SSP1-2.6 scenario. The northwest region is expected to experience an increase in the number of droughts, trends in drought, and drought frequency under the other three scenarios. The most severe drought conditions were observed under the SSP5-8.5 scenario. This study provides insights into the spatial and temporal development of drought in Northwest China using meteorological and model data. The findings can serve as a basis for drought risk assessment, scientific water resources management, and agricultural production in the region.

Key words: SPEI, spatial-temporal pattern of drought, CMIP6, Northwest China

山建安, 朱睿, 尹振良, 杨华庆, 张薇, 方春爽. 基于CMIP6模式的中国西北地区干旱时空变化[J]. 干旱区研究, 2024, 41(5): 717-729.

SHAN Jian'an, ZHU Rui, YIN Zhenliang, YANG Huaqing, ZHANG Wei, FANG Chunshuang. Spatial and temporal variation of drought in Northwest China based on CMIP6 model[J]. Arid Zone Research, 2024, 41(5): 717-729.

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参考文献 37

[1]	杨晋云, 张莎, 白雲, 等. 基于机器学习融合多源遥感数据模拟SPEI监测山东干旱[J]. 中国农业气象, 2021, 42(3): 230-242.
	[Yang Jinyun, Zhang Sha, Bai Yun, et al. SPEl simulation for monitoring drought based machine learning lntegrating multi-source remote sensing data in Shandong[J]. Chinese Journal of Agrometeorology, 2021, 42(3): 230-242.]
[2]	赵海燕, 张文千, 邹旭恺, 等. 气候变化背景下中国农业干旱时空变化特征分析[J]. 中国农业气象, 2021, 42(1): 69-79.
	[Zhao Hanyan, Zhang Wenqian, Zou Xukai, et al. Temporal and spatial characteristics of drought in China under climate change[J]. Chinese Journal of Agrometeorology, 2021, 42(1): 69-79.]
[3]	Wang S, Li R, Wu Y, et al. Effects of multi-temporal scale drought on vegetation dynamics in Inner Mongolia from 1982 to 2015, China[J]. Ecological Indicators, 2022, 136: 108666.
[4]	Crocetti L, Forkel M, Fischer M, et al. Earth observation for agricultural drought monitoring in the Pannonian Basin (Southeastern Europe): Current state and future directions[J]. Regional Environmental Change, 2020, 20(4): 123.
[5]	Dai A. Drought under global warming: A review[J]. Wiley Interdisciplinary Reviews: Climate Change, 2011, 2(1): 45-65.
[6]	王鹏涛. 西北地区干旱灾害时空统计规律与风险管理研究[D]. 西安: 陕西师范大学, 2018.
	[Wang Pengtao. Study on Spatial-temporal Statistics and Risk Management of Drought Disaster in Northwest China[D]. Xi'an: Shaanxi Normal University, 2018.]
[7]	陈亚宁, 李忠勤, 徐建华, 等. 中国西北干旱区水资源与生态环境变化及保护建议[J]. 中国科学院院刊, 2023, 38(3): 385-393.
	[Chen Yaning, Li Zhongqin, Xu Jianhua, et al. Changes and protection suggestions in water resources and ecological environment in arid region of Northwest China[J]. Bulletin of Chinese Academy of Sciences, 2023, 38(3): 385-393.]
[8]	Seier B A, Haves M, Bresan L. Usino the standardized precipitation index for tood risk monitorina[J]. Intematonal Journal of Cimatoloay, 2002, 22: 1365-1379
[9]	Palmer W C. Meteorological Drought[R]. Washington DC: U. S. Department of Commerce, Weather Bureau, 1965: 45-58.
[10]	张强, 高歌. 我国近50年旱涝灾害时空变化及检测预警服务[J]. 科技导报, 2004, 3(4): 109-114.
	[Zhang Qiang, Gao Ge. The spatial and temporal features of drought and flood disasters in the past 50 years and monitoring and warning services in China[J]. Science & Technology Review, 2004, 3(4): 109-114.]
[11]	Vicente-Serrano S M, Begueria S, Lopez-Moreno J. A mul-tiscalar drought index sensitive to global warming: The stan-dardized precipitation evapotranspiration index[J]. Journal of Climate, 2010, 23(7): 1696-1718.
[12]	李伟光, 易雪, 侯美亭, 等, 基于标准化降水蒸散指数的华南干旱趋势研究[J]. 中国生态农业学报, 2012, 20(5): 643-649.
	[Li Weiguang, Yi Xue, Hou Meiting, et al. Study on drought trend in south China based on standardized precipitation evapotranspiration index[J]. Journal of Natural Disasters, 2012, 20(5): 643-649.]
[13]	薛华柱, 李阳阳, 董国涛. 基于SPEI 指数分析河西走廊气象干旱时空变化特征[J]. 中国农业气象, 2022, 43(11): 923-934.
	Xue Huazhu, Li Yangyang, Dong Guotao. Analysis of spatial-temporal variation characteristics of meteorological drought in the Hexi Corridor based on SPEI index[J]. Chinese Journal of Agrometeorology, 2022, 43(11): 923-934.] doi: 10.3969/j.issn.1000-6362.2022.11.006
[14]	刘哲琼, 佘敦先, 夏军, 等. 黄土高原地区气象干旱动态格局演变及其对植被的影响[J]. 地理科学, 2023, 43(9): 1659-1671. doi: 10.13249/j.cnki.sgs.2023.09.016
	[Liu Zheqiong, She Dunxian, Xia Jun, et al. Impacts of meteorological drought on vegetation and their dynamic pattern evolutions on the Loess Plateau[J]. Scientia Geographica Sinica, 2023, 43(9): 1659-1671.] doi: 10.13249/j.cnki.sgs.2023.09.016
[15]	侯青青, 裴婷婷, 陈英, 等. 1986—2019年黄土高原干旱变化特征及趋势[J]. 应用生态学报, 2021, 32(2): 649-660. doi: 10.13287/j.1001-9332.202102.012
	[Hou Qingqing, Pei Tingting, Chen Ying, et al. Variations of drought and its trend in the Loess Plateau from 1986 to 2019[J]. Chinese Journal of Applied Ecology, 2021, 32(2): 649-660.] doi: 10.13287/j.1001-9332.202102.012
[16]	姚俊强, 毛炜峄, 陈静, 等. 新疆气候“湿干转折”的信号和影响探讨[J]. 地理学报, 2021, 76(1): 57-72. doi: 10.11821/dlxb202101005
	[Yao Junqiang, Mao Weiyi, Chen Jing, et al. Trend of climate and hydrology change in Xinjiang and its problems thinking[J]. Journal of Glaciology and Geocryology, 2021, 76(1): 57-72.]
[17]	刘珂, 姜大膀. 基于两种潜在蒸散发算法的SPEI对中国干湿变化的分析[J]. 大气科学, 2015, 39(1): 23-36.
	[Liu Ke, Jiang Dabang. Analysis of dryness/wetness over China using standardized precipitation evapotranspiration index based on two evapotranspiration algorithms[J]. Chinese Journal of Atmospheric Sciences, 2015, 39 (1): 23-36.]
[18]	卢冬燕, 朱秀芳, 刘婷婷, 等. 2 ℃温升情景下中国气象干旱特征变化[J]. 干旱区地理, 2023, 46(8): 1227-1237.
	[Lu Dongyan, Zhu Xiufang, Liu Tingting, et al. Changes in meteorological drought characteristics in China under the 2 ℃ temperature rise scenario[J]. Arid Land Geography, 2023, 46(8): 1227-1237.]
[19]	胡实, 莫兴国, 林忠辉. 未来气候情景下我国北方地区干旱时空变化趋势[J]. 干旱区地理, 2015, 38(2): 239-248.
	[Hu Shi, Mo Xingguo, Lin Zhonghui. Projections of spatial-temporal variation of drought in north China[J]. Arid Land Geography, 2015, 38(2): 239-248.]
[20]	Li S Y, Miao L J, Jiang Z H, et al. Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015-2099[J]. Advances in Climate Change Research, 2020, 11(3): 210-217.
[21]	丁一汇, 柳艳菊, 徐影, 等. 全球气候变化的区域响应: 中国西北地区气候“暖湿化”趋势、成因及预估研究进展与展望[J]. 地球科学进展, 2023, 38(6): 551-562. doi: 10.11867/j.issn.1001-8166.2023.027
	[Ding Yihui, Liu Yanju, Xu Ying, et al. Regional responses to global climate change: Progress and prospects for trend, causes, and projection of climatic warming-wetting in Northwest China[J]. Advances in Earth Science, 2023, 38(6): 551-562.] doi: 10.11867/j.issn.1001-8166.2023.027
[22]	姚旭阳, 张明军, 张宇, 等. 中国西北地区气候转型的新认识[J]. 干旱区地理, 2022, 45(3): 671-683.
	[Yao Xuyang, Zhang Mingjun, Zhang Yu, et al. New insights into climate transition in Northwest China[J]. Arid Land Geography, 2022, 45(3): 671-683.]
[23]	粟晓玲, 褚江东, 张特, 等. 西北地区地下水干旱时空演变趋势及对气象干早的动态响应[J]. 水资源保护, 2022, 38(1): 34-42.
	[Su Xiaoling, Chu Jiangdong, Zhang Te, et al. Spatio-temporal evolution trend of groundwater drought and its dynamic response to meteorological drought in Northwest China[J]. Water Resources Protection, 2022, 38(1): 34-42.]
[24]	孔蕊. 变化环境下西北地区草地地上碳储量演变及驱动力分析[D]. 南京: 南京林业大学, 2020.
	[Kong Rui. Evolution and Driving Force Analysis of Grassland Aboveground Carbon Storage in Northwest China Under Changing Environment[D]. Nanjing: Nanjing Forestry University, 2020.]
[25]	李雅培, 朱睿, 刘涛, 等. 基于BCC-CSM2-MR模式的疏勒河流域未来气温降水变化趋势分析[J]. 高原气象, 2021, 40(3): 535-546. doi: 10.7522/j.issn.1000-0534.2020.00078
	[Li Yapei, Zhu Rui, Liu Tao, et al. Trend analysis of future temperature and precipitation in Shule river basin based on BCC-CSM2-MR model[J]. Plateau Meteorology, 2021, 40(3): 535-546.] doi: 10.7522/j.issn.1000-0534.2020.00078
[26]	Kharin V V, Zwiers F W. Climate predictions with multimodel ensembles[J]. Journal of Climate, 2002, 15(7): 793-799.
[27]	周天军, 邹立维, 陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述[J]. 气候变化研究进展, 2019, 15(5): 445-456.
	[Zhou Tianjun, Zou Liwei, Chen Xiaolong. Commentary on the coupled model intercomparison project phase 6 (CMIP6)[J]. Climate Change Research, 2019, 15(5): 445-456.]
[28]	胡一阳, 徐影, 李金建, 等. CMIP6不同分辨率全球气候模式对中国降水模拟能力评估[J]. 气候变化研究进展, 2021, 17(6): 730-743.
	[Hu Yiyang, Xu Ying, Li Jinjian, et al. Evaluation on the performance of CMIP6 global climate models with different horizontal resolution in simulating the precipitation over China[J]. Climate Change Research, 2021, 17(6): 730-743.]
[29]	黄文君. 中国西北干旱区干旱时空演变及预估[D]. 乌鲁木齐: 新疆大学, 2021.
	[Huang Wenjun. Spatial-temporal Variation and Projection of Drought in the Arid Regions of Northwestern China[D]. Urumqi: Xinjiang University, 2021.]
[30]	王东, 张勃, 安美玲, 等. 基于SPEI的西南地区近53 a干旱时空特征分析[J]. 自然资源学报, 2014, 29(6): 1003-1016. doi: 10.11849/zrzyxb.2014.06.009
	[Wang Dong, Zhang Bo, An Meiling, et al. Temporal and spatial distributions of drought in Southwest China over the past 53 years based on Standardized Precipitation Evapotranspiration Index[J]. Journal of Natural Resources, 2014, 29(6): 1003-1016.] doi: 10.11849/zrzyxb.2014.06.009
[31]	王兆礼, 黄泽勤, 李军, 等. 基于SPEI和NDVI的中国流域尺度气象干旱及植被分布时空演变[J]. 农业工程学报, 2016, 32(14): 177-186.
	[Wang Zhaoli, Huang Zeqing, Li Jun, et al. Assessing impacts of meteorological drought on vegetation at catchment scale in China based on SPEI and NDVI[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(14): 177-186.]
[32]	郑景云, 尹云鹤, 李炳元. 中国气候区划新方案[J]. 地理学报, 2010, 65(1): 3-12.
	[Zheng Jingyun, Yin Yunhe, Li Bingyuan, et al. A new scheme for climate regionalization in China[J]. Acta Geographica Sinica, 2010, 65(1): 3-12.] doi: 10.11821/xb201001002
[33]	Mehrotra R, Sharma A. A robust alternative for correcting systematic biases in multi-variable climate model simulations[J]. Environmental Model Software, 2021, 139: 105019
[34]	杨肖丽, 马慧君, 吴凡, 等. 基于CMIP6的全球及干旱带干旱时空演变[J]. 水资源保护, 2023, 39(2): 1004-6993.
	[Yang Xiaoli, Ma Huijun, Wu Fan, et al. Spatiotemporal evolution of global and arid zone drought based on CMIP6[J]. Water Resources Protection, 2023, 39(2): 1004-6993.]
[35]	Zhu H, Jiang Z, Li J, et al. Does CMIP6 inspire more confidence in simulating climate extremes over China?[J]. Advances in Atmospheric Sciences, 2020, 37: 1119-1132.
[36]	Ma Z, Sun P, Zhang Q, et al. The characteristics and evaluation of future droughts across China through the CMIP6 multi-model ensemble[J]. Remote Sensing, 2022, 14(5): 1097.
[37]	田佳西. 西北地区气候暖湿化演变趋势及其对植被恢复影响研究[D]. 南京: 南京林业大学, 2023.
	[Tian Jiaxi. Study on the Evolution Trend of Climate Warming and Wetting in Northwest China and Its Impact on Vegetation Restoration[D]. Nanjing: Nanjing Forestry University, 2023.]

模式名称	研发机构	格点数/个
ACCESS-CM2	澳大利亚联邦科学与工业研究组织	192×144
ACCESS-ESM1-5	澳大利亚联邦科学与工业研究组织	192×144
CanESM5	加拿大环境署	128×64
CMCC-ESM2	欧洲地中海气候变化中心	288×192
EC-Earth3	欧盟地球系统模式联盟	512×256
FGOALS-g3	中国科学院大气物理研究所	180×80
GFDL-ESM4	美国大气海洋局	288×180
INM-CM4-8	俄罗斯科学院计算数学研究所	180×120
INM-CM5-0	俄罗斯科学院计算数学研究所	180×120
IPSL-CM6A-LR	皮埃尔-西蒙拉普拉斯研究所	144×143
MIROC6	日本海洋地球科学与技术处	256×128
MPI-ESM1-2-HR	德国气候和地球系统研究中心	384×192
MPI-ESM1-2-LR	德国马普气象研究所	192×96
MRI-ESM2-0	日本气象厅气象研究所	320×160
NorESM2-MM	挪威气候中心	288×192
TaiESM1	“中研院”	288×192

干旱等级	SPEI值
无旱	SPEI>-0.5
轻旱	-1.0<SPEI≤-0.5
中旱	-1.5<SPEI≤-1.0
重旱	-2.0<SPEI≤-1.5
特旱	SPEI≤-2.0

	SSP1-2.6				SSP2-4.5				SSP3-7.0				SSP5-8.5
	轻旱	中旱	重旱	特旱	轻旱	中旱	重旱	特旱	轻旱	中旱	重旱	特旱	轻旱	中旱	重旱	特旱
年	20.17	14.77	7.14	2.04	20.57	15.37	7.51	2.08	20.88	15.5	7.4	1.91	21.17	16.82	8.27	1.83
春	17.46	12.4	5.78	1.61	18.24	13.12	6.09	1.71	18.66	13.04	5.88	1.51	18.66	13.69	6.67	1.8
夏	15.4	10.16	4.6	1.16	15.84	10.81	4.72	1.24	16.84	11.31	4.89	1.14	16.23	10.89	4.77	1.21
秋	17.08	12.17	5.61	1.63	17.57	12.59	5.96	1.67	17.7	12.39	5.7	1.48	17.59	13.42	6.7	1.76
冬	17.68	12.62	6.04	1.64	18.52	13.67	6.47	1.7	19.14	13.73	6.38	1.51	19.09	14.52	7.04	1.66

基于CMIP6模式的中国西北地区干旱时空变化

Spatial and temporal variation of drought in Northwest China based on CMIP6 model

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